Research on Transformer Oil Level Monitoring System Based on Inspection Robot

Because the traditional oil level gauge transformer relies on manual regular patrol detection, it is not easy to know the state change of the oil level in real time. In recent years, the rapid development of AR/VR technology, the positioning, and the construction of maps of robots have also received widespread attention, and the oil level inspection task has gradually evolved in the direction of intelligent machine inspection. Based on this background, this paper studies the Cartographer algorithm, according to the mapping results for path planning, to achieve robot autonomous navigation of the designated location. After arriving at the destination, the user obtains the oil level by the depth camera, and uses the YOLOv4 to design an intelligent detection algorithm for oil level detection, using neural networks to learn the oil level state in various weather, and train a dedicated model. Through experimental verification, the inspection robot can complete the construction of the map and realize autonomous path navigation according to the target point, and the oil level detection system based on the YOLOv4 algorithm can achieve an accuracy of more than 90% for the recognition of oil level information, which has good applicability.